






"""simple docstring"""





import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Optional[Any]  )    ->							Any:
						__magic_name__:	int							=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
						__magic_name__:	Optional[int]							=       Image.open(requests.get(__UpperCAmelCase      ,   stream=__UpperCAmelCase  ).raw  ).convert("""RGB"""  )

						__magic_name__:	Union[str, Any]							=       transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)      ,   interpolation=InterpolationMode.BICUBIC  ),
						        transforms.ToTensor(),
						        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)      ,   (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)  ),
						    ]  )
						__magic_name__:	Any							=       transform(__UpperCAmelCase  ).unsqueeze(0  ).to(__UpperCAmelCase  )
						return image



def   a    (     __UpperCAmelCase      :    Any  )    ->							Optional[Any]:
						if "visual_encoder" in key:
												__magic_name__:	Union[str, Any]							=       re.sub("""visual_encoder*"""      ,   """vision_model.encoder"""      ,   __UpperCAmelCase  )
						if "blocks" in key:
												__magic_name__:	Optional[Any]							=       re.sub(R"""blocks"""      ,   """layers"""      ,   __UpperCAmelCase  )
						if "attn" in key:
												__magic_name__:	Optional[Any]							=       re.sub(R"""attn"""      ,   """self_attn"""      ,   __UpperCAmelCase  )
						if "norm1" in key:
												__magic_name__:	Tuple							=       re.sub(R"""norm1"""      ,   """layer_norm1"""      ,   __UpperCAmelCase  )
						if "norm2" in key:
												__magic_name__:	List[str]							=       re.sub(R"""norm2"""      ,   """layer_norm2"""      ,   __UpperCAmelCase  )
						if "encoder.norm" in key:
												__magic_name__:	Optional[int]							=       re.sub(R"""encoder.norm"""      ,   """post_layernorm"""      ,   __UpperCAmelCase  )
						if "encoder.patch_embed.proj" in key:
												__magic_name__:	Dict							=       re.sub(R"""encoder.patch_embed.proj"""      ,   """embeddings.patch_embedding"""      ,   __UpperCAmelCase  )

						if "encoder.pos_embed" in key:
												__magic_name__:	List[Any]							=       re.sub(R"""encoder.pos_embed"""      ,   """embeddings.position_embedding"""      ,   __UpperCAmelCase  )
						if "encoder.cls_token" in key:
												__magic_name__:	Dict							=       re.sub(R"""encoder.cls_token"""      ,   """embeddings.class_embedding"""      ,   __UpperCAmelCase  )

						if "self_attn" in key:
												__magic_name__:	Optional[Any]							=       re.sub(R"""self_attn.proj"""      ,   """self_attn.projection"""      ,   __UpperCAmelCase  )

						return key



@torch.no_grad()
def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Optional[int]=None  )    ->							Tuple:
						if config_path is not None:
												__magic_name__:	Optional[Any]							=       BlipConfig.from_pretrained(__UpperCAmelCase  )
						else:
												__magic_name__:	str							=       BlipConfig(projection_dim=5_1_2      ,   text_config={}      ,   vision_config={}  )

						__magic_name__:	Union[str, Any]							=       BlipForConditionalGeneration(__UpperCAmelCase  ).eval()

						__magic_name__:	List[str]							=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

						__magic_name__:	Optional[Any]							=       blip_decoder(pretrained=__UpperCAmelCase      ,   image_size=3_8_4      ,   vit="""base"""  )
						__magic_name__:	Optional[Any]							=       pt_model.eval()

						__magic_name__:	int							=       pt_model.state_dict()
						for key in modified_state_dict.copy():
												__magic_name__:	Tuple							=       modified_state_dict.pop(__UpperCAmelCase  )
												__magic_name__:	str							=       rename_key(__UpperCAmelCase  )
												__magic_name__:	Dict							=       value

						hf_model.load_state_dict(__UpperCAmelCase  )

						__magic_name__:	Optional[int]							=       3_8_4
						__magic_name__:	List[Any]							=       load_demo_image(image_size=__UpperCAmelCase      ,   device="""cpu"""  )
						__magic_name__:	int							=       BertTokenizer.from_pretrained("""bert-base-uncased"""  )
						__magic_name__:	Any							=       tokenizer(["""a picture of"""]  ).input_ids

						__magic_name__:	List[str]							=       hf_model.generate(__UpperCAmelCase      ,   __UpperCAmelCase  )

						assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

						__magic_name__:	List[str]							=       hf_model.generate(__UpperCAmelCase  )

						assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

						if pytorch_dump_folder_path is not None:
												hf_model.save_pretrained(__UpperCAmelCase  )

						# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
						__magic_name__:	str							=       (
						    """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
						)

						__magic_name__:	Optional[int]							=       blip_vqa(pretrained=__UpperCAmelCase      ,   image_size=__UpperCAmelCase      ,   vit="""base"""  )
						vqa_model.eval()

						__magic_name__:	Optional[Any]							=       vqa_model.state_dict()
						for key in modified_state_dict.copy():
												__magic_name__:	Dict							=       modified_state_dict.pop(__UpperCAmelCase  )
												__magic_name__:	List[str]							=       rename_key(__UpperCAmelCase  )
												__magic_name__:	Optional[int]							=       value

						__magic_name__:	Tuple							=       BlipForQuestionAnswering(__UpperCAmelCase  )

						hf_vqa_model.load_state_dict(__UpperCAmelCase  )

						__magic_name__:	Any							=       ["""How many dogs are in this image?"""]
						__magic_name__:	Tuple							=       tokenizer(__UpperCAmelCase      ,   return_tensors="""pt"""  ).input_ids

						__magic_name__:	Optional[int]							=       hf_vqa_model.generate(__UpperCAmelCase      ,   __UpperCAmelCase  )
						print(tokenizer.decode(answer[0]  )  )

						assert tokenizer.decode(answer[0]  ) == "[UNK] 1 [SEP]"
						if pytorch_dump_folder_path is not None:
												hf_vqa_model.save_pretrained(pytorch_dump_folder_path + """_vqa"""  )

						__magic_name__:	Optional[Any]							=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

						__magic_name__:	Any							=       blip_itm(pretrained=__UpperCAmelCase      ,   image_size=__UpperCAmelCase      ,   vit="""base"""  )
						itm_model.eval()

						__magic_name__:	Union[str, Any]							=       itm_model.state_dict()
						for key in modified_state_dict.copy():
												__magic_name__:	int							=       modified_state_dict.pop(__UpperCAmelCase  )
												__magic_name__:	Tuple							=       rename_key(__UpperCAmelCase  )
												__magic_name__:	str							=       value

						__magic_name__:	Optional[Any]							=       BlipForImageTextRetrieval(__UpperCAmelCase  )

						__magic_name__:	Dict							=       ["""A picture of a woman with a dog sitting in a beach"""]
						__magic_name__:	int							=       tokenizer(
						    __UpperCAmelCase      ,   return_tensors="""pt"""      ,   padding="""max_length"""      ,   truncation=__UpperCAmelCase      ,   max_length=3_5      ,   ).input_ids

						hf_itm_model.load_state_dict(__UpperCAmelCase  )
						hf_itm_model.eval()

						__magic_name__:	Optional[int]							=       hf_itm_model(__UpperCAmelCase      ,   __UpperCAmelCase      ,   use_itm_head=__UpperCAmelCase  )
						__magic_name__:	Optional[Any]							=       hf_itm_model(__UpperCAmelCase      ,   __UpperCAmelCase      ,   use_itm_head=__UpperCAmelCase  )

						assert out[0].item() == 0.21_10_68_74_94_27_79_54
						assert torch.nn.functional.softmax(out_itm[0]      ,   dim=1  )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

						if pytorch_dump_folder_path is not None:
												hf_itm_model.save_pretrained(pytorch_dump_folder_path + """_itm"""  )


if __name__ == "__main__":
							__lowerCamelCase							=  argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							__lowerCamelCase							=  parser.parse_args()

							convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  List[Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''timm_backbone'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_,   )       ->   Tuple:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     backbone
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		Optional[Any]							      =     features_only
						UpperCAmelCase_:		Any							      =     use_pretrained_backbone
						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     out_indices if out_indices is not None else (-1,)






